Abstract. Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) observations span an entire annual cycle of Arctic snow and sea ice cover. However, the measurements of atmospheric and ocean forcing, as well as distributed measurements of snow and ice properties, were occasionally interrupted for logistical reasons. The most prolonged interruption happened during the onset of the summer melt season. Here we introduce and apply a novel data–model fusion system that can assimilate relevant observational data in a collection of modeling tools (SnowModel-LG and HIGHTSI) to provide continuous high-temporal-resolution (3-hourly) time series of snow and sea ice parameters over the entire annual cycle. We used this system to analyze differences between the three main ice types found in the MOSAiC Central Observatory: relatively deformed second-year ice, second-year ice with extensive smooth refrozen melt pond surfaces, and first-year ice. Since SnowModel-LG and HIGHTSI were used in a 1-D configuration, we used a sea ice dynamics term D to parameterize the redistribution of snow to newly created ridges and leads. D correlated highly with the sea ice deformation (R2 = 59 %, N=33) in the vicinity of the observatory, and deformation appears to explain as much as 15 % of all winter snow water equivalent. In addition, we show, in separate simulations for level ice, that snow bedforms with thin snow in the bedform troughs largely control the ice growth. Here, the mean snow depth minus 1 standard deviation was required to simulate realistic sea ice thickness using HIGHTSI; we surmise that this accounts for the control of relatively thin snow on local ice growth. Despite different initial sea ice thickness and freeze-up dates, the sea ice thickness of level ice across all ice types became similar by early winter. Our simulations suggest that the mean (spatially distributed) MOSAiC snowmelt onset began in late May but was interrupted by a snowfall event and was delayed by 3 weeks until mid- June. The level ice started to melt in the last week of June. Depending on the sea ice topography, the ice was snow-free by late June and early July.

Millionen Menschen in Europa fehlt Wasser, Lebensmittel werden noch teurer. Der aktuelle Klimaschutz kann diese Welt nicht verhindern. Was das für unser Leben bedeutet

Abstract. Accurate weather and climate forecasting relies heavily on the precise modeling of sea ice, a critical component of the Earth's climate system. Sea ice influences global weather patterns, ocean circulation, and the exchange of heat and moisture between the atmosphere and oceans. Initialisation of the sea ice component of global coupled models relies on data assimilation techniques to incorporate information from observations to constrain the system. This study focuses on the development of sea ice data assimilation for ECMWF’s latest Ocean Reanalysis System 6 (ORAS6) that includes a multicategory sea ice model. The research addresses the challenge of appropriately distributing sea ice concentration increments across various thickness categories in the model. Here, we show that using a simple proportional increment splitting method improves the accuracy of sea ice concentration analyses compared to previous approaches. Our findings indicate that adding an additional sea ice-sea water temperature balance brings further performance benefits. These results suggest that the choice of increment distribution strategy significantly impacts the accuracy of sea ice representation in reanalysis systems. The system presented here will form the basis of ECMWF's data assimilation system for numerical weather prediction, as well as the next generation coupled reanalyses.